Apparatus and method for determining a composition of a replacement therapy treatment

ABSTRACT

An apparatus and method for determining a composition of a replacement therapy treatment is presented, the apparatus at least a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to receive a user input wherein the user input comprises at least an identifier and a constitutional history of the user, generate a first condition descriptor as a function of the user input, determine a composition of a replacement therapy treatment as a function of the first condition descriptor, wherein the determination comprises training a first machine-learning process using user training data, wherein the user training data correlates user inputs to compositions of the replacement therapy treatment and determining the composition as a function of the user input and the first machine learning process, and output the composition of the replacement therapy treatment as a function of the determination.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. ProvisionalPatent Application Ser. No. 63/213,271, filed on Jun. 22, 2021, andtitled “METHODS AND SYSTEMS FOR DETERMINING A COMPOSITION OF AREPLACEMENT THERAPY TREATMENT,” which is incorporated by referenceherein in its entirety.

FIELD OF THE INVENTION

The present invention generally relates to the field of medicine. Inparticular, the present invention is directed to an apparatus and methodfor determining a composition of a replacement therapy treatment.

BACKGROUND

Replacement therapy is widely used to treat many ailments. Most commonuses for replacement therapy are the replacing a lost nutrient orsubstance. For example, hormone replacement therapy is primarily used totreat menopausal effects or osteoporosis by treatment with estrogens orprogestogens. Other types of replacement therapies aim at epidermalhydration, skin elasticity, skin thickness, and also reduces skinwrinkles. Implementing such therapies to treat major ailments can bechallenging.

SUMMARY OF THE DISCLOSURE

In one aspect, an apparatus for determining a composition of areplacement therapy treatment is presented. The apparatus comprises atleast a processor and a memory communicatively connected to theprocessor, the memory containing instructions configuring the at least aprocessor to receive a user input wherein the user input comprises atleast an identifier and a constitutional history of the user, generate afirst condition descriptor as a function of the user input, determine acomposition of a replacement therapy treatment as a function of thefirst condition, wherein the determination comprises training a firstmachine-learning process using user training data, wherein the usertraining data correlates user inputs to compositions of the replacementtherapy treatment and determining the composition as a function of theuser input and the first machine learning process, and output thecomposition of the replacement therapy treatment as a function of thedetermination.

In another aspect of the disclosure, a method for determining acomposition of a replacement therapy treatment, the method comprisesreceiving, at a processor, a user input, wherein the user inputcomprises at least an identifier and a constitutional history of theuser, generating, at the computing device, a first condition descriptoras a function of the user input, determining, at a processor, acomposition of a replacement therapy treatment as a function of thefirst condition, wherein the determination comprises training a firstmachine-learning process using user training data, wherein the usertraining data correlates user inputs to compositions of the replacementtherapy treatment and determining the composition as a function of theuser input and the first machine learning process, and outputting, at aprocessor, the composition of the replacement therapy treatment as afunction of the determination.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an exemplary embodiment of an apparatus fordetermining a composition of a replacement therapy treatment;

FIG. 2 is a block diagram of an exemplary embodiment of a database;

FIG. 3 is a block diagram of an exemplary embodiment of amachine-learning module;

FIG. 4 is a block diagram of an exemplary embodiment of a replacementtherapy treatment;

FIG. 5 is a flow diagram illustrating an exemplary embodiment of amethod for determining a composition of a replacement therapy treatment;and

FIG. 6 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed to anapparatus and method for determining a composition of a replacementtherapy treatment. The apparatus comprises at least a processor and amemory communicatively connected to the processor, the memory containinginstructions configuring the at least a processor to perform the stepsdescribed herein. The processor may receive a user input wherein theuser input comprises at least an identifier and a constitutional historyof the user. The apparatus may also generate a first conditiondescriptor as a function of the user input. Aspects of the presentdisclosure may also include determining a composition of a replacementtherapy treatment as a function of the first condition descriptorwherein the determination comprises training a first machine-learningprocess using user training data wherein the user training datacorrelates user inputs to compositions of the replacement therapytreatment and determining the composition as a function of the userinput and the first machine learning process. The system may alsoinclude outputting the composition of the replacement therapy treatmentas a function of the determination.

Now referring to FIG. 1 , an exemplary embodiment of a block diagram ofan apparatus for determining a composition of a replacement therapytreatment is illustrated. Apparatus 100 comprises at least a processorand a memory communicatively connected to the processor, the memorycontaining instructions configuring the at least a processor to performsteps as described herein. At least a processor may be a computingdevice 104. Computing device 104 may include any computing device asdescribed in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Computing device104 may include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. Computing device 104 mayinclude a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. Computing device 104 may interface or communicate with one ormore additional devices as described below in further detail via anetwork interface device. Network interface device may be utilized forconnecting computing device 104 to one or more of a variety of networks,and one or more devices. Examples of a network interface device include,but are not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A networkmay employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, softwareetc.) may be communicated to and/or from a computer and/or a computingdevice. Computing device 104 may include but is not limited to, forexample, a computing device or cluster of computing devices in a firstlocation and a second computing device or cluster of computing devicesin a second location. Computing device 104 may include one or morecomputing devices dedicated to data storage, security, distribution oftraffic for load balancing, and the like. Computing device 104 maydistribute one or more computing tasks as described below across aplurality of computing devices of computing device, which may operate inparallel, in series, redundantly, or in any other manner used fordistribution of tasks or memory between computing devices. Computingdevice 104 may be implemented using a “shared nothing” architecture inwhich data is cached at the worker, in an embodiment, this may enablescalability of system 100 and/or computing device.

With continued reference to FIG. 1 , computing device 104 may bedesigned and/or configured to perform any method, method step, orsequence of method steps in any embodiment described in this disclosure,in any order and with any degree of repetition. For instance, Computingdevice 104 may be configured to perform a single step or sequencerepeatedly until a desired or commanded outcome is achieved; repetitionof a step or a sequence of steps may be performed iteratively and/orrecursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Computing device 104 may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing.

Still referring to FIG. 1 , computing device 104 is configured toreceive a user input 108. User input 108 may be received from a userdevice 112. In this disclosure, a “user input” is a piece of datareceived from a user, possibility through a remote user device. “Userdevice” may include without limitation, a display in communication withcomputing device 104, where a display may include any display asdescribed herein. User device may include an additional computingdevice, such as a mobile device, laptop, desktop, computer, and thelike. In an embodiment, user device may have a touch screen to interactwith the user. User may input user input 108 through user device. Any ofuser inputs 108 may be input via user inputs 108 at user device 112,and/or retrieved from database 120. Additionally, user device 112 mayuse a remote sensor to obtain user input 108. A “remote sensor,” as usedin this disclosure, is a device that captures data of human subject andtransmits that data to computing device 104, either by transmitting thedata to user device which relays the data to computing device 104, or bytransmitting the data separately over a network connection. User input108 may be transmitted via communication channel interface and/or via aseparate network connection formed, for instance, using a secure socketslayer (SSL) and/or hypertext transfer protocol-secure (HTTPS) process.User inputs 108 may include any data indicative of a person'sphysiological state; physiological state may be evaluated with regard toone or more measures of health of a person's body, one or more systemswithin a person's body such as a circulatory system, a digestive system,a nervous system, or the like, one or more organs within a person'sbody, and/or any other subdivision of a person's body useful fordiagnostic or prognostic purposes. User input 108 may include, but notlimited to any medical test, a user's health assessment, a user'sconstitutional history, an assessment conducted in any website providinginformation related to a medical condition, a direct entry from a user,and the like. As a non-limiting example, and without limitation, userinput 108 describing red blood cells may be recognized as useful foridentifying various conditions such as dehydration, high testosterone,nutrient deficiencies, kidney dysfunction, chronic inflammation, anemia,and/or blood loss. For instance, and without limitation, a particularset of biomarkers, test results, and/or biochemical information may berecognized in each medical field as useful for identifying variousdisease conditions or prognoses within a relevant field.

Continuing to refer to FIG. 1 , user input 108 comprises at least anidentifier. The input may include an identifier. An “identifier” as usedin this specification is a unique, non-changing alphanumeric set ofcharacters of any length for each user. Identifier may link a user to amedical record, or an electronic health record associated with the user.The identifier may include a specific sequence of characters, numbers,letters, and/or words that may identify a user. The identifier mayinclude a globally recognized uniform identifier such as a uniform codecommission (UCC) barcode. The identifier may be directly entered intocomputing device 104. For example, a barcode reader or an electronic penmay be used to enter the identifier directly into computing device 104.The identifier may accompany a request for laboratory services and/orrequest for a procedure. Moreover, in a non-limiting embodiment,identifier may include text strings describing user information. Forexample and without limitation, identifier may include name, contactinformation, constitutional history, illnesses, appointment history,test results etc. Person of ordinary skill in the art, upon reviewingthe entirety of this disclosure, will be aware of highlighting termsand/or the various types of information and/or qualifications associatedwith a user that are relevant to generating first condition descriptorand the like thereof.

Continuing to refer to FIG. 1 , user input 108 comprises aconstitutional history of the user. As defined in this disclosure, the“constitutional history” of the user is defined as the user's medicalpast and present which may contain relevant information bearing on theuser's medical past, present, and future. The constitutional history ofthe user may include, but not limited to the chief concern; the historyof present illness, past constitutional history which may includepreexisting illnesses, medication history, and allergies; the familyhistory which may include the family constitutional history as well asfamily behavioral history; the user's social history, questions relatedto the user's organs which may help establish the causes of signs andsymptoms, and the like. The constitutional history of the user may beproblem-focused, expanded problem-focused, comprehensive, and the like.

User input 108 may include a plurality of capillary densitymeasurements. As defined in this disclosure, “capillary density” refersto the length of red cell-perfused capillaries per observation area(cm-1). Capillary density may include one or more measurements ofglycocalyx thickness, one or more measurements of perfused boundaryregion, and/or a microvascular health score. For instance and withoutlimitation, capillary density may include a Microvascular Health Score(MVHS) as produced by Glycocheck of Maastricht, Netherlands. Forinstance, capillary density refers to the number of capillaries presentat a certain site in the human body. Measuring capillary density mayprovide information that may help diagnose a user with a potentialdisease. For instance, loss of capillary density, and thus flow of bloodthrough tissues, may be considered a feature of aging. Such loss ofblood flow may provide an indication that a user may be at risk for, forexample, heart disease. In another non-limiting example, loss ofcapillary density may be associated with connective tissue diseases(“CTD”). “CTD,” as used in this disclosure, are a diverse group ofrheumatologic disorders characterized by the presence of autoantibodiesand systemic organ involvement, frequently including the lung or chest.An experimental setup may include, but not limited to a sidestream darkfield (“SDF”) camera (CapiScope HVCS, KK Technology, Honiton, UK). wasused to visualize the sublingual microvasculature. The dynamic lateralmovement of red blood cells (“RBCs”) is measured which provides anindication as to the capillary density. The plurality of capillarydensity measurements are measured using sublingual video microscopy. Inthis case, the camera, such as the SDF camera, is positioned towards thesublingual mucosa and maneuvered until a clear image of themicrocirculation is acquired. As an example, the camera may use greenlight emitting stroboscopic diodes (540 nm) to detect the hemoglobin ofpassing red blood cells (RBCs). With the use of, for example, a 5×objective with a 0.2 numerical aperture, images are captured, providinga 325-fold magnification in 720×576 pixels at 23 frames per second. Eachcomplete measurement may consist of, at least ten 2-second videos (40frames/video), containing a total of about 3000 vascular segments of 10μm length each. All videos are deliberately obtained from differentpositions to counterbalance spatial heterogeneity of the sublingualmicrocirculation.

Additionally or alternatively, with continued reference to FIG. 1 , userinput 108 may include, without limitation, hematological data, such asred blood cell count, which may include a total number of red bloodcells in a person's blood and/or in a blood sample, hemoglobin levels,hematocrit representing a percentage of blood in a person and/or samplethat is composed of red blood cells, mean corpuscular volume, which maybe an estimate of the average red blood cell size, mean corpuscularhemoglobin, which may measure average weight of hemoglobin per red bloodcell, mean corpuscular hemoglobin concentration, which may measure anaverage concentration of hemoglobin in red blood cells, platelet count,mean platelet volume which may measure the average size of platelets,red blood cell distribution width, which measures variation in red bloodcell size, absolute neutrophils, which measures the number of neutrophilwhite blood cells, absolute quantities of lymphocytes such as B-cells,T-cells, Natural Killer Cells, and the like, absolute numbers ofmonocytes including macrophage precursors, absolute numbers ofeosinophils, and/or absolute counts of basophils. User input 108 mayinclude, without limitation, immune function data such as Interleukine-6(IL-6), TNF-alpha, systemic inflammatory cytokines, and the like.

Continuing to refer to FIG. 1 , user input 108 may include, withoutlimitation, data describing blood-born lipids, including totalcholesterol levels, high-density lipoprotein (HDL) cholesterol levels,low-density lipoprotein (LDL) cholesterol levels, very low-densitylipoprotein (VLDL) cholesterol levels, levels of triglycerides, and/orany other quantity of any blood-born lipid or lipid-containingsubstance. User input 108 may include measures of glucose metabolismsuch as fasting glucose levels and/or hemoglobin A1-C (HbA1c) levels.User input may include, without limitation, one or more measuresassociated with endocrine function, such as without limitation,quantities of dehydroepiandrosterone (DHEAS), DHEA-Sulfate, quantitiesof cortisol, ratio of DHEAS to cortisol, quantities of testosteronequantities of estrogen, quantities of growth hormone (GH), insulin-likegrowth factor 1 (IGF-1), quantities of adipokines such as adiponectin,leptin, and/or ghrelin, quantities of somatostatin, progesterone, or thelike. User input 108 may include measures of estimated glomerularfiltration rate (eGFR). User input 108 may include quantities ofC-reactive protein, estradiol, ferritin, folate, homocysteine,prostate-specific Ag, thyroid-stimulating hormone, vitamin D, 25hydroxy, blood urea nitrogen, creatinine, sodium, potassium, chloride,carbon dioxide, uric acid, albumin, globulin, calcium, phosphorus,alkaline phosphatase, alanine amino transferase, aspartate aminotransferase, lactate dehydrogenase (LDH), bilirubin, gamma-glutamyltransferase (GGT), iron, and/or total iron binding capacity (TIBC), orthe like. User input 108 may include peptides, lipid analysis, growthfactors, micro-RNA, RNA, and genetic data. User input 108 may includeone or more markers including but not limited to antinuclear antibodylevels, Rheumatoid factor, Sjogren's antibodies, Anti-Tubulin,Associated with alcoholic liver disease, demyelinating disease, Grave'sdisease, Hashimoto's thyroiditis, infectious agent exposurePANDAS/ANDAS/OCD, rheumatoid arthritis, and recent onset type 1diabetes, Anti-Myelin basic protein, Related to the risk for multiplesclerosis, autism, PANDAS/ANDAS/OCD, and systemic lupus erythematosus(SLE), an autoimmune condition, COVID-19, Anti-Myelin oligodendrocyteglycoprotein (MOG), Found in various demyelinating diseases, includingmultiple sclerosis, neuromyelitis optica spectrum disorders (NMOSD),idiopathic optic neuritis (ON), acute disseminated encephalomyelitis(ADEM), multiphasic disseminated encephalomyelitis (MDEM), Devic'sdisease, and tumefactive demyelinating disease, Anti-Myelin proteolipidprotein, A useful marker in patients with seronegative anti-myelin basicprotein, the frequent marker in active multiple sclerosis and opticneuritis, Anti-Neurofascin, Found mainly in combined central andperipheral demyelination (CCPD), a rare demyelinating conditionaffecting both CNS and peripheral nervous system (PNS) tissues, and alsoin chronic inflammatory demyelinating polyneuropathy (CIDP) and axonalinjury in patients with multiple sclerosis (MS), Anti-MAG, Anti-MAGperipheral neuropathy is a very rare disease caused by anti-MAGantibodies that destroy MAG protein leading to disruptions of normalmyelin production and healthy peripheral nerve activity.

With continued reference to FIG. 1 , user input 108 may include one ormore markers of blood brain barrier disruption, including but notlimited to Anti-s100b, Blood brain barrier integrity breach andsub-concussive episodes lead to the production of this antibody.Extravasated s100B may trigger a pathologic autoimmune reaction linkingsystemic and CNS immune responses, Anti-Glial fibrillary acidic protein,Anti-GFAP is produced when the protein enters the bloodstream after arupture of the blood brain barrier, thus serves as a blood baseddiagnostic marker of brain injury, Anti-Microglia, Indicate adestruction of the blood brain barrier and are found to play a role intissue destruction of Alzheimer's disease, Anti-Glucose regulatedprotein 78, Glucose-regulated protein 78-targeted antibodies couldinstigate blood brain barrier breakdown and development of hallmarkanti-aquaporin-4 autoantibody pathology.

With continued reference to FIG. 1 , user input 108 may include one ormore markers of optical and/or autonomic nervous system disordersincluding but not limited to Anti-Neuron specific enolase, Antibodiesagainst neuron specific enolase are found in patients with opticalneuropathies, Anti-Aquaporin 4, AQP4 IgG is involved in the developmentof neuromyelitis optica and revolutionized the understanding of thedisease. Anti-Aquaporin4 antibodies have also been shown in patientswith peripheral demyelination, Anti-Recoverin, One of the key componentsof antibody disorders of the CNS. They have also been shown to beassociated with retinopathy which is characterized by impaired visionand photosensitivity, Anti-CV2, Seen in autoimmune paraneoplasticautonomic neuropathy and mixed axonal and demyelinating peripheralneuropathy and the like.

With continued reference to FIG. 1 , user input 108 may include one ormore markers of peripheral neuropathy, including but not limited toAnti-GM1, Associated with multi-focal motor neuropathy and lower motorneuropathy, characterized by muscle weakness and atrophy, Anti-GM2, Apotential peripheral nerve antigen for neuropathy-associatedautoantibodies, Anti-Hu, The most frequent manifestation of sensoryneuropathy with frequent autonomic involvement, Anti-Ri, Can be detectedin patients with the paraneoplastic opsoclonus/myoclonus syndrome.Neoplasms most often associated with anti-Ri include breast cancer,gynecological cancers, and small cell lung cancer, Anti-Amphiphysin,Often found in the serum of patients with stiff-person syndrome and thelike.

With continued reference to FIG. 1 , user input 108 may include one ormore markers of neuromuscular disorders, including but not limited toAnti-Acetylcholine receptors, Found in myasthenia gravis disease whichdestroys the receptor function, leading to a neuromuscular transmissiondefect, which then causes hypofunction, fatigue, and inflammation ofskeletal muscles and produces serum antibodies against muscle antigens,Anti-Muscle specific kinase, An important marker in patients withoutanti-acetylcholine receptor antibodies in myasthenia gravis disease,Anti-Voltage gated calcium channels, Responsible for Lambert-Eatonmyasthenic syndrome (LEMS), a rare autoimmune disorder of theneuromuscular junction, Anti-Voltage gated potassium channels,Downregulate the potassium channels expressed on the peripheral nerveterminal leading to nerve hyperexcitability, Anti-Titin, Present in70-90% of thymoma autoimmune myasthenia gravis (MG) patients, and inapproximately 50% of late-onset acetylcholine-MG patients withoutthymoma and the like.

With continued reference to FIG. 1 , user input 108 may include one ormore markers of brain autoimmunity, including but not limited toAnti-Purkinje cell, Autoimmunity to a class of GABAergic neurons locatedin the cerebellum, which can produce abnormalities and decline in grossmotor functions, Anti-Yo, Suggest that a patient with neurologicsymptoms has a paraneoplastic syndrome. In addition, their presence alsooften suggests the nature of the underlying tumor, Anti-Amyloid beta(25-35), Levels of autoantibodies reacting with oligomers of the short,neurotoxic fragment Aβ (25-35) are significantly higher in AD patientsthan in healthy controls, Anti-Amyloid beta (1-42), A signature markerin Alzheimer's disease, Anti-RAGE peptide, Found in Alzheimer's diseasepatients, and particularly higher in AD patients with diabetes,Anti-Tau, Found in the neurofibrillary tangles in brains of individualswho have Alzheimer's disease, Anti-Glutamate, Found in epilepsy,encephalitis, cerebellar ataxia, systemic lupus erythematosus (SLE) andneuropsychiatric SLE, Sjogren's syndrome, schizophrenia, mania orstroke, Anti-Dopamine, Associated with movement disorders characterizedby parkinsonism, dystonia, and Sydenham chorea, Anti-Hydroxytryptamine,Found mainly in autoimmune encephalitis, Anti-Alpha-synuclein, Mainlyelevated in Parkinson's disease and Alzheimer's disease, Anti-α1 and β2adrenergic receptors, Found mainly in patients with different dementiaforms such as unclassified, Lewy body, vascular, and Alzheimer'sdementia, Anti-Endothelin A receptor, Found in vascular dementia and thelike.

With continued reference to FIG. 1 , user input 108 may include one ormore markers of brain inflammation, including but not limited toAnti-NMDA receptor, Found in anti-NMDA receptor encephalitis, Anti-AMPAreceptor, May play a role in Alzheimer's disease, characterized bydecreased AMPA activation and synapse loss, Anti-Dopamine receptors,Associated with Parkinson's disease and other disorders of low dopaminestatus, Anti-GABA receptors, Associated with temporal lobe epilepsy(TLE), Parkinson's disease (PD) and Huntington's disease (HD) and otherneurodegenerative disorders that involve disruptions in gamma-aminobutyric acid (GABA) signaling, Anti-Dipeptidyl aminopeptidase-likeprotein 6, Associated with encephalitis, Anti-Glycine receptor, Helpfulin the diagnosis of patients with symptoms and signs that include ocularmotor and other brainstem dysfunction, hyperekplexia, stiffness,rigidity, myoclonus and spasms, Anti-Neurexin 3, Associated with asevere but potentially treatable encephalitis in which the antibodiescause a decrease of neurexin-3a and alter synapse development,Anti-Contactin-associated protein-like 2, Diseases associated withCNTNAP2 include Pitt-Hopkins-Like Syndrome 1 and Autism 15,Anti-Leucine-rich glioma-inactivated protein 1, LGI1 antibody-associatedencephalitis has increasingly been recognized as a primary autoimmunedisorder, Anti-Ma, Present in men with testicular tumors and isolated orcombined limbic encephalitis (LE), diencephalic encephalitis (DE), orbrainstem encephalitis (BE) and the like.

With continued reference to FIG. 1 , user input 108 may include one ormore markers of infection, including but not limited to Anti-HSV-1,HSV-1 has been reported to have a pathogenesis role in Herpes simplexencephalitis (HSE) and seropositivity to HSV-1 antibodies has beencorrelated with increased risk of Alzheimer's disease, Anti-HSV-2,Herpes simplex encephalitis (HSE) is a disorder commonly associated withHSV-2. HSE due to HSV-2 may occur without meningitis features.Antibodies against HSV-2 have shown positive correlation in patientswith symptoms of HSE, Anti-EBV, Antibodies against the EBV nuclearantigen complex (EBNAc) and EBNA-1 have been correlated with increasedrisk of multiple sclerosis (MS), Anti-CMV, Cytomegalovirus (CMV)infections have been reported frequently to be associated withGuillain-Barre syndrome (GBS). There is a potential for molecularmimicry between GM2 and antigens induced by CMV infection, Anti-HHV-6,Human herpesvirus-6 (HHV-6) is frequently associated with neurologicdiseases, including multiple sclerosis (MS), epilepsy, encephalitis, andfebrile illness, Anti-HHV-7, HHV-7 has been less frequently associatedwith CNS disease than HHV-6, but found to be associated withencephalitis, meningitis, and demyelinating conditions. Similar to HHV6A, increased levels of HHV-7 were found in multiple brain regions inAlzheimer's disease (AD) patients, Anti-Streptococcal A,Anti-streptococcal A antibodies are shown to cross react with differentbrain proteins that could lead to neuropsychiatric symptoms includingPANDAS characterized by pediatric obsessive-compulsive disorder and thelike.

With continued reference to FIG. 1 , user input 108 may include one ormarkers including but not limited to aluminum, mercury, lead, cadmium,or arsenic levels. User input 108 may include arsenic levels. User input108 may include levels of fibrinogen, plasma cystatin C, and/or brainnatriuretic peptide, Neuron specific enolase (NSE), Glial fibrillaryacidic protein (GFAP), Amyloid beta, Abeta 1-42, Abeta 1-40,Abeta42/Abeta40 ratio, Total Tau, s100b, Neurofilament light, alphasynuclein, Brain-derived neurotrophic factor (BNDF) and the like. In anembodiment, user input 108 may be obtained from one or more measurementsof blood and/or cerebrospinal fluid (CSF).

Continuing to refer to FIG. 1 , user input 108 may include measures oflung function such as forced expiratory volume, one second (FEV-1) whichmeasures how much air can be exhaled in one second following a deepinhalation, forced vital capacity (FVC), which measures the volume ofair that may be contained in the lungs. User input may include ameasurement blood pressure, including without limitation systolic anddiastolic blood pressure. User input may include a measure of waistcircumference. User input 108 may include body mass index (BMI) ormeasurements of Intracellular and Extracellular Water, Phase Angle, Bodycomposition, lean body mass, fat mass, All measured via BioimpedenceAnalysis. User input may include one or more measures of bone massand/or density such as dual-energy x-ray absorptiometry. User input 108may include one or more measures of muscle mass. User input 108 mayinclude one or more measures of physical capability such as withoutlimitation measures of grip strength, evaluations of standing balance,evaluations of gait speed, pegboard tests, timed up and go tests, and/orchair rising tests.

With continued reference to FIG. 1 , user input 108 may include, withoutlimitation any result of any medical test and/or physiologicalassessments, or the like. A medical test may include but is not limitedto a positron emission tomography (PET) scan, a computed tomography scan(CT), a magnetic resonance imaging (MRI), ultrasound, anelectroencephalogram (EEG), a quantitative EEG, any neuropsychologicaltesting and the like. For instance, user input may include any medicaltests and/or results used to diagnose a renal disorder, such as aglomerular filtration rate test (“GFR”). “GFR,” as used in thisdisclosure, is a urine test to check for albumin. Albumin is a proteinthat can pass into the urine when the kidneys are damaged. In anembodiment, user input 108 may include any marker of autoantibodies,toxicity, inflammation, cellular senescence, autophagy, mitochondrialfunction, neurodegeneration and the like.

Still referring to FIG. 1 , user input 108 may include othercardiovascular data such as heart rate data, blood pressure data, or thelike, for instance captured using audio and/or oximetry devices. Userinput may include respiratory data such as audio capture of pulmonarysounds using a microphone or the like. User input 108 may includeneurological data. User input 108 may include digestive audio data. Userinput 108 may include visual data captured regarding one or moreelements of externally visible patient anatomy. User input 108 maycapture one or more elements of human subject bodily motion, includinggait, posture or gestural motions. In an embodiment, User input 108 mayinclude glycocalyx-related biomarkers, as explained above.

Additionally, user device may use a remote sensor to obtain user input108. A “remote sensor,” as used in this disclosure, is a device thatcaptures data of human subject and transmits that data to computingdevice 104, either by transmitting the data to user input device whichrelays the data to computing device 104, or by transmitting the dataseparately over a network connection. User input 108 may be transmittedvia communication channel interface and/or via a separate networkconnection formed, for instance, using a secure sockets layer (SSL)and/or hypertext transfer protocol-secure (HTTPS) process. Remote sensormay include, without limitation, a camera such as a digital cameraincorporated in a mobile device or the like, a microphone such as amobile device microphone, a motion sensor, which may include one or moreaccelerometers, gyroscopes, magnetometer, or the like. Remote sensor mayinclude one or more peripheral devices such as a peripheral pulseoximeter or the like. Remote sensor may include a network-connecteddevice such as a network connected digital scale or the like. In anembodiment, remote sensor may be used to capture audio or visual dataconcerning one or more portions of human subject's anatomy. Forinstance, and without limitation, a microphone may be pressed againstone or more portions of human subject at direction of user overcommunication channel, causing capture of audio data from the one ormore portion of human subject; as a non-limiting example, audio data ofhuman subject lungs, heart, digestive system, or the like may be socaptured. As a further example, user may instruct human subject to traina camera on one or more portions of anatomy to capture visual dataconcerning such one or more portions. Such physiological data may becombined; for instance, audio capture of circulatory system noise datamay be combined with pulse oximetry data from a peripheral pulseoximeter and/or motion-sensor data indicating a degree of activity.Remote sensor may include an electrical sensor such as a portableelectrocardiogram device or the like. Generally, any sensor capable ofcapturing data of human subject and transmitting such data locally orover a network may be used as a remote sensor.

Still referring to FIG. 1 , computing device 104 is also configured togenerate a first condition descriptor 116 as a function of user input108. Computing device 104 may generate first condition descriptor 116 asa function of user input 108 or may be received within user input 108.As defined in this disclosure, “first condition descriptor” is datadescribing a first condition; a “first condition” is a disease, illness,or injury that may be treated or in need of treatment by a medicalprofessional of a replacement therapy treatment. A first conditiondescriptor 116 may include any physiologic, mental, or psychologicalcondition or disorder. Non-limiting examples of first conditiondescriptor 116 may include atherosclerosis, cardiovascular disease,adult cancer, arthritis, cataracts, osteoporosis, type 2 diabetes,hypertension, neurodegeneration (including but not limited toAlzheimer's disease, Huntington's disease, and other age-progressivedementias; Parkinson's disease; and amyotrophic lateral sclerosis[ALS]), stroke, atrophic gastritis, osteoarthritis, NASH, camptocormia,chronic obstructive pulmonary disease, coronary artery disease, dopaminedysregulation syndrome, metabolic syndrome, effort incontinence,Hashimoto's thyroiditis, heart failure, late life depression,immunosenescence (including but not limited to age related decline inimmune response to vaccines, age related decline in response toimmunotherapy etc.), myocardial infarction, acute coronary syndrome,sarcopenia, sarcopenic obesity, senile osteoporosis, urinaryincontinence etc. A first condition descriptor may be related to achange in blood parameters, heart rate, cognitive functions/decline,bone density, basal metabolic rate, systolic blood pressure, heel bonemineral density (BMD), heel quantitative ultrasound index (QUI), heelbroadband ultrasound attenuation, heel broadband ultrasound attenuation,forced expiratory volume in 1-second (FEV1), forced vital capacity(FVC), peak expiratory flow (PEF), duration to first press ofsnap-button in each round, reaction time, mean time to correctlyidentify matches, hand grip strength (right and/or left), whole bodyfat-free mass, leg fat-free mass (right and/or left), and time forrecovery after any stress (wound, operation, chemotherapy, disease,change in lifestyle etc.). Embodiments of the first condition descriptormay include a cardiovascular disease, bone loss disorder, aneuromuscular disorder, a neurodegenerative disorder or a cognitivedisorder, a metabolic disorder, sarcopenia, osteoarthritis, chronicfatigue syndrome, senile dementia, mild cognitive impairment,schizophrenia, Huntington's disease, Pick's disease, Creutzfeldt-Jakobdisease, stroke, CNS cerebral senility, age-related cognitive decline,pre diabetes, diabetes, obesity, osteoporosis, coronary artery disease,cerebrovascular disease, heart attack, stroke, peripheral arterialdisease, aortic valve disease, stroke, Lewy body disease, amyotrophiclateral sclerosis (ALS), mild cognitive impairment, pre-dementia,dementia, progressive subcortical gliosis, progressive supranuclearpalsy, thalamic degeneration syndrome, hereditary aphasia, myoclonusepilepsy, macular degeneration, or cataracts, hair loss, hair greying,and the like. Additionally, first condition descriptor 116 may be causedby a pathogen such as bacteria, archaea, protists, fungi, infectionsproteins such as prions, parasitic multicellular organisms such asnematodes including without limitation ascarids and/or filarial worms,flatworms including without limitation flukes and tapeworms, insectoidparasites such as without limitation botflies and/or screw worms, or thelike. In an embodiment, first condition descriptor 116 may be caused bya corona virus. In this disclosure, a “corona virus” refers to a largefamily of viruses that cause illness ranging from the common cold tomore severe diseases.

Still referring to FIG. 1 , computing device 104 may connect to and/orinclude a database 120. Database 120 may be implemented, withoutlimitation, as a relational database 120, a key-value retrieval database120 such as a NOSQL database 120, or any other format or structure foruse as a database 120 that a person skilled in the art would recognizeas suitable upon review of the entirety of this disclosure. Database 120may alternatively or additionally be implemented using a distributeddata storage protocol and/or data structure, such as a distributed hashtable or the like. Database 120 may include a plurality of data entriesand/or records as described above. Data entries in a database 120 may beflagged with or linked to one or more additional elements ofinformation, which may be reflected in data entry cells and/or in linkedtables such as tables related by one or more indices in a relationaldatabase 120. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which data entries ina database 120 may store, retrieve, organize, and/or reflect data and/orrecords as used herein, as well as categories and/or populations of dataconsistently with this disclosure. In some embodiments, network data, orother information such as user information, transfer party information,and alimentary provider information, may be stored in and/or retrievedfrom database 120. Database 120 is further described herein withreference to FIG. 2 .

Referring still to FIG. 1 , generating first condition descriptor 116may further comprise identifying a plurality of biomarkers as a functionof user input 108. A “biomarker”, as used in this disclosure, is abiological and/or chemical substance or process that is indicative of aparticular functioning in the body. Plurality of biomarkers may include,without limitation, red blood cell count, hemoglobin levels, hematocrit,mean corpuscular volume, mean corpuscular hemoglobin, and/or meancorpuscular hemoglobin concentration. The presence of at least onebiomarker may indicate a likelihood that a user is currentlyexperiencing or might experience some disease at a future date. Forinstance, early detection of tumor necrosis factor-alpha (TNF) from theTNF cytokine family which triggers many intracellular processes mayindicate that the user may be experiencing or will experience symptomsof, rheumatoid arthritis, for example. Plurality of biomarkers mayinclude, for example, monitoring biomarkers. A “monitoring biomarker,”as used in this specification, is a biomarker that may be used to assessthe progress of a disease or to monitor the effects of a therapeuticagent, such as, for example, administration of a course of antibiotics.In another example, a biomarker may be a diagnostic biomarker. A“diagnostic biomarker,” as defined in this disclosure is a biomarkerthat is used to detect the presence of a disease or a condition ofinterest. Another example of a biomarker is a predictive biomarker. A“predictive biomarker,” as used in this disclosure, is a biomarker usedto predict what group of patients will respond favorably or unfavorablyto a particular treatment. In an embodiment, plurality of biomarkers mayinclude a predictive biomarker. Examples of plurality of biomarkers thatmay be used in diagnosing a, for instance, type-2 diabetes may includebranched chain amino acids (BCAA) which may be associated withhyperglycemia and may be a predictive biomarker for type-2 diabetes.Other potential predictive biomarkers of diabetes risk includedimethylglycine (DMG), 2-amino adipic acid, and glycine. Plurality ofbiomarkers may be extracted, for example, chemically. For instance, anenzyme-linked immunosorbent assay (“ELISA”) may be used to identify atleast one disease biomarker. For instance, the presence of InterleukinIL-10 (IL-1(3) and/or matrix metalloproteinase (MMP-9) may indicate thepotential for the presence of a renal disease. Plurality of biomarkersmay be extracted, for example, from a research journal. Alternatively,plurality of biomarkers may be extracted by experimentation. Forexample, a biomarker that may indicate a particular disease mayincorporate testing for the presence of a biomarker using a controlgroup where the group does not have the biomarker present. Values forthe biomarker for a sample group known to have the biomarker present maybe compared against the values obtained for the control group and adetermination made regarding the presence of a particular disease.

Still referring to FIG. 1 , generating first condition descriptor 116may further comprise calculating a value for each biomarker of theplurality of biomarkers in a biological sample. As used in thisdisclosure, a “value” is a numerical measure of presence of a particularbiomarker. In an embodiment, the plurality of biomarkers may bedetermined by statistical methods. A kidney disorder, for example, maybe indicated by changes in a value for each biomarker within aparticular range of neutrophil gelatinase-associated lipocalin (NGAL). Avalue for each biomarker within a suitable range for a marker may, forexample, indicate the absence of a particular health condition. A valueoutside the suitable range may indicate, for example, the presence of aparticular health condition. A value for each biomarker consideredoutside the suitable range may indicate a value that is higher or lowerthan a value included within the suitable range. A value for eachbiomarker may be a value published in, for example, a research journal.Alternatively, a value for each biomarker may be determined byexperimentation. For example, an analysis of a renal disorder mayinclude a control experiment to determine the values of a particularvalue for each biomarker that fall within the suitable range. After thecontrol experiment, a urine sample, for instance, may be analyzed and ameasurement for a value for a particular biomarker made and compared tothe value of the control sample. A patient may be suffering from a renaldisorder if, for example, the value of a value for a particularbiomarker falls outside the suitable range of values of the value forthe control experiment. A value for the value for a biomarker that ishigher or lower than the suitable range may result in a positive resultfor a renal disorder.

For example, data reduction methods may be used to obtain the type ofbiomarkers. Data reduction statistical methods may include, but notlimited to trend analysis, clustering, and the like. Classificationalgorithms may also be utilized to determine the plurality ofbiomarkers. Such statistical methods include, but not limited to,regression, support vector machine, decision trees and random forests,artificial neural networks, and gene relationship analysis. Anothercategory of statistical methods may include visualization. Statisticalmethods in this category may include, but not limited to, PrincipalComponent Analysis, Network analysis, and the like. Values forbiomarkers may be determined from a biological sample. A “biologicalsample” as used in this example, may include any sample obtained from ahuman body of a user. A physical sample may be obtained from a bodilyfluid and/or tissue analysis such as a blood sample, tissue, sample,buccal swab, mucous sample, stool sample, hair sample, fingernail sampleand the like. A physical sample may be obtained from a device in contactwith a human body of a user such as a microchip embedded in a user'sskin, a sensor in contact with a user's skin, a sensor located on auser's tooth, and the like. Biomarkers may be determined from thephysical sample and transmitted to computing device 104.

Referring still to FIG. 1 , computing device 104 is further configuredto determine a composition 124 of a replacement therapy treatment as afunction of first condition descriptor 116. As used herein, a“replacement therapy treatment” is an process for remediation of atherapeutic health problem and/or imbalance, where the process forremediation includes replacement of plasma in the user's bloodstream.Replacement therapy treatment may include plasma exchange treatment. Asused in this specification, a “plasma exchange treatment” is defined asthe removing plasma from the body and the replacing the plasma withplasma replacement therapy treatment. “Plasma,” as used in thisdisclosure, is the liquid portion of blood. Health conditions suitablefor plasma exchange treatment may include, without limitation, treatmentof neurological conditions such as Guillain-Barre Syndrome, chronicinflammatory demyelinating polyneuropathy, Alzheimer's disease,Parkins's disease, unspecified neurodegenerative conditions and thelike. Non-neurologic conditions such as Myasthenia Gravis,hyperviscosity syndrome, thrombotic thrombocytopenic purpura, haemolyticuremic syndrome, idiopathic thrombocytopenia, long COVID, PASC and thelike, may be suitable for plasma exchange treatment. Other conditionsmay include, but not limited to, transplant rejection of solid organssuch as the kidneys and heart, multiple sclerosis, and the like.Additionally, replacement therapy treatment may include a composition ofplasma protein concentrate that is used to replace human plasma in thebloodstream of the user, as explained below. In an embodiment, plasmaexchange treatment and replacement therapy may be utilized to improvecellular habitat so as to modify tissued based and/or exogenous stemcells behavior to effect multi-tissue regeneration. Replacement therapytreatment may include a series of one or more ingredients given to auser at the same time and/or in a sequence of treatments given our aspecified period of time. For example, replacement therapy may include asingle infusion containing two ingredients given to a user in the courseof one treatment. In yet another non-limiting example, a replacementtherapy may include a series of infusions containing a multiple ofingredients, with each ingredient given in a particular series of stepsand at a particular period of time within the treatment. In anembodiment, replacement therapy treatment may be delivered to a userwith any delivery mechanism including but not limited to oral delivery,intravenous delivery, subcutaneous delivery, intranasal delivery, rectaldelivery, vaginal delivery, dermal delivery and the like. In anembodiment, replacement therapy treatment may include one or moreadditional therapies given before, during, and/or after delivery ofreplacement therapy treatment. An “additional therapy,” as used in thisdisclosure, is any therapy given in addition to replacement therapytreatment. An additional therapy may include but is not limited to aprescription medication, supplement, food, exercise program, over thecounter medication, a homeopathic remedy, a natural medicine, an herbalextract and the like. For example, a user may be prescribed an oralsupplement such as bromelain 400 mg to be taken twice daily for 3 daysprior to receiving a replacement therapy treatment.

In this disclosure, a “composition” refers to the makeup and ingredientsused to create the replacement therapy treatment. Composition 124 of thereplacement therapy treatment may comprise albumin. Albumin may bepresent in an amount ranging from at least about 30 grams per liter toabout 60 grams per liter. Additionally, composition 124 may comprise aplasma protein concentrate. Composition 124 of the replacement therapytreatment may comprise a solution of electrolytes. Composition 124 mayinclude one or more immunoglobulins, trace mineral , complex biologicalmolecules such as extracts or derivations of plant material, biosimilarmolecules, molecules intended for the purpose of chelation of cationictoxins, and the like. Composition 124 of the replacement therapytreatment may comprise lipids. Composition 124 of the replacementtherapy treatment may comprise proteins and peptides. Composition 124 ofthe replacement therapy treatment may comprise immunoglobinconcentrates. Composition 124 of the replacement therapy treatment maycomprise stem cells and products of stem cells such as exosomes.Composition 124 of the replacement therapy treatment may comprisevitamins. Composition 124 of the replacement therapy treatment maycomprise ions. Furthermore, generating first condition descriptor 116further comprises identifying a plurality of biomarkers as a function ofthe user input 108, calculating a value for each biomarker of theplurality of biomarkers in a biological sample, training a secondmachine-learning process using a reference biomarker training datawherein the reference biomarker training data correlates referencevalues for each biomarker to first conditions 116, and determining firstcondition descriptor 116 as a function of the value for each biomarkerof the plurality of biomarkers and the second machine learning process.

Determining composition 124 may include training a firstmachine-learning process 128 using user training data 132. As usedherein, “training data,” as used herein, is data containing correlationsthat a machine-learning process may use to model relationships betweentwo or more categories of data elements. User training data 132 may bereceived and/or collected from experts or from users that may have mayhave been diagnosed with a health condition with particular diseasemarkers where exchange treatment improved and/or cure the healthcondition. User training data 132 may be received as a function ofdeterminations of a health condition based on disease markers, healthcondition metrics, and/or measurable values. User training data 132 setmay be received and/or otherwise developed during one or more pastiterations of the previous user training data vectors. User trainingdata 132 may be received from one or more remote devices that at leastcorrelate a biomarker and its correlating value to a composition 124,where a remote device is an external device to computing device 104. orinstance, and without limitation, training data may include a pluralityof data entries, each entry representing a set of data elements thatwere recorded, received, and/or generated together; data elements may becorrelated by shared existence in a given data entry, by proximity in agiven data entry, or the like. Multiple data entries in training datamay evince one or more trends in correlations between categories of dataelements; for instance, and without limitation, a higher value of afirst data element belonging to a first category of data element maytend to correlate to a higher value of a second data element belongingto a second category of data element, indicating a possible proportionalor other mathematical relationship linking values belonging to the twocategories. Multiple categories of data elements may be related in usertraining data 132 according to various correlations; correlations mayindicate causative and/or predictive links between categories of dataelements, which may be modeled as relationships such as mathematicalrelationships by machine learning processes as described in furtherdetail below. User training data 132 may be formatted and/or organizedby categories of data elements, for instance by associating dataelements with one or more descriptors corresponding to categories ofdata elements. As a non-limiting example, user training data 132 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in usertraining data 132 may be linked to descriptors of categories by tags,tokens, or other data elements; for instance, and without limitation,user training data 132 may be provided in fixed-length formats, formatslinking positions of data to categories such as comma-separated value(CSV) formats and/or self-describing formats such as extensible markuplanguage (XML), enabling processes or devices to detect categories ofdata. User training data 132 correlates user inputs 108 to compositions124 of the replacement therapy treatment. Composition 124 is thendetermined as a function of user input 108 and first machine learningprocess 128. The determination of the composition may further includedetermining the composition, where the first machine-learning processreceives a first condition descriptor as an input and outputs thecomposition of the replacement therapy treatment. Computing device 104outputs the composition of the replacement therapy treatment to the userdevice.

Referring still to FIG. 1 , computing device 104 may be configured totrain a second machine-learning process using a reference biomarkertraining data to generate first condition descriptor 116. Referencebiomarker training data may correlate reference values for eachbiomarker to first conditions 116. First condition descriptor 116 maythen be determined as a function of the value for each biomarker of theplurality of biomarkers and the second machine learning process. Thereference biomarker training data may then correlate a reference valuefor each biomarker to a first condition. Computing device 104 may thendetermine first condition descriptor 116 where the secondmachine-learning model receives the value for biomarker as an input andoutputs a first condition. The machine-learning process is describedbelow in this disclosure with reference to FIG. 3 . In an embodiment,computing device 104 may iteratively regenerate the reference biomarkertraining data as a function of first condition. The secondmachine-learning model is retrained using the regenerated biomarkertraining data. For example, new conditions that may involve newbiomarkers may be incorporated into the training data and correct formodel drift or predictive performance degradation.

Computing device 104 may then iteratively regenerate the referencebiomarker training data as a function of the first condition descriptorand retrain the second machine-learning process using regeneratedbiomarker training data. As used in this disclosure, “iterativelyregenerate” means repeatedly update the training data every time eachfirst condition descriptor 116 is outputted. The second machine-learningmodel may be retrained using the regenerated biomarker training data.second machine-learning model may be trained, for example but withoutlimitation, after each time the training data us updated, after acertain period of times, after the training data is updated a certainnumber of times, or the like. For example, new conditions that mayinvolve new biomarkers may be incorporated into the training data andcorrect for model drift or predictive performance degradation.

Still referring to FIG. 1 , computing device 104 is further configuredto output composition 124 of the replacement therapy treatment as afunction of the determination. For example, the composition may beoutputted to a label that may be attached to an IV bag, a syringe, orthe like. The composition may be outputted to the patient's ElectronicHealth Record where it can be retrieved and processed by a medicalprofessional. The composition may be outputted to a location such as,but not limited to a compounding pharmacy or a laboratory equipped toprepare such compositions, where the composition may be outputted.

Referring back to FIG. 1 , computing device 104 may be configured todetermine the composition 124 of the replacement therapy treatment totreat a plurality of conditions. Plurality of conditions may occur atthe same time. As used in this disclosure, a “plurality of conditions”are at least two health-related conditions that may affect the same ordifferent systems in the human body where the patient is suffering fromthese conditions simultaneously. For example, one composition may beused in the replacement therapy treatment to treat a patient sufferingfrom a cardiovascular ailment and a kidney issue. Another example mayinclude a composition that may be used to treat a patient suffering fromdiabetic foot ulcers and diabetic neuropathy as a result of type-2diabetes.

With continued reference to FIG. 1 , system 100 further comprises arobot where the robot is designed and configured to prepare thecomposition of the replacement therapy treatment. As used herein, a“robot” refers to a programmable machine capable of carrying out acomplex series of actions. Examples of robots include the ArXium(Buffalo Grove, Ill.) Riva™ system or the Grifols (Los Angeles, Calif.)KIRO® Fill Automated system or the Gri-fill Sterile Compounding Systgemand Gri-bag. Robot may include a plurality of components, such asservos. As used herein, “servos” refer to servo motors, which are rotaryor linear actuators that rotate and push parts of a machine withprecision. Another type of motor that the robot may also include isstepper motors. “Stepper motors” are DC motors that move in discretesteps by having multiple coils that are organized into groups; the rotorwill rotate one step at a time. Robot may also be programmed through acommunicative connection. As used in this disclosure, “communicativelyconnected” means connected by way of a connection, attachment or linkagebetween two or more relata which allows for reception and/ortransmittance of information therebetween.

Furthermore, robot may also have a sensor. As used in this disclosure, a“sensor” is a device that is configured to detect a phenomenon andtransmit information and/or datum related to the detection of thephenomenon. For instance, and without limitation, a sensor may transforman electrical and/or nonelectrical stimulation into an electrical signalthat is suitable to be processed by an electrical circuit, such as acontroller which is further explained below. A sensor may generate asensor output signal, which transmits information and/or datum relatedto a detection by the sensor. A sensor output signal may include anysignal form described in this disclosure, such as for example, digital,analog, optical, electrical, fluidic, and the like. In some cases, asensor, a circuit, and/or a controller may perform one or more signalprocessing steps on a signal. For instance, a sensor, circuit, and/orcontroller may analyze, modify, and/or synthesize a signal in order toimprove the signal, for instance by improving transmission, storageefficiency, or signal to noise ratio.

Robot may also possess an embedded processor or microcontroller separatefrom computing device 104. Microcontroller may include any sort ofcomputing device. Microcontroller may include any computing device asdescribed in this disclosure with reference to FIGS. 1 and 6 , includingwithout limitation a microcontroller, microprocessor, digital signalprocessor (DSP) and/or system on a chip (SoC) as described in thisdisclosure.

The robot may be configured to prepare a syringe that includes thecomposition of the replacement therapy treatment. The robot may beconfigured to prepare syringes with a volume of replacement therapytreatment ranging from about 1 ml to 60 ml. The robot may be configuredto prepare an intravenous (IV) bag that includes the composition of thereplacement therapy treatment. The robot may be configured to prepare anIV bag with a volume of replacement therapy treatment ranging from about25 ml to 1,000 ml. The robot may be programmed to determine the exactdimensions of the IV bag. For example, the robot may be equipped with abarcode reader where the robot scans a barcode placed outside the bag,where the barcode contains the size of the bag. Once the size of the bagis determined, the movement by the robot may be optimized by using, forexample, geometric programming or any other optimization algorithm. Therobot may be configured to generate a label. For example, the label maycontain, without limitation, a barcode containing the identifyinginformation of a patient, the composition of the replacement therapytreatment, the date of compounding, an expiration date for theformulation, a signatory line, and the like.

Now referring to FIG. 2 , an exemplary embodiment of a database 120 isillustrated. Database 120 may, as a non-limiting example, organize datastored in the database according to one or more database tables. One ormore database tables may be linked to one another by, for instance,common column values. “Common column values” are the sums of the valuesin all the expanded data cells in that column at the current rowlocation. For instance, a common column between two tables of database120 may include an identifier of a first condition, for instance asdefined below; as a result, a query may be able to retrieve all rowsfrom any table pertaining to a given first condition. Other columns mayinclude any other category usable for organization or subdivision ofdata, including types of data, common pathways between, for example, analimentary combination and a first alimentary provider, or the like;persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which expert data from oneor more tables may be linked and/or related to expert data in one ormore other tables.

Still referring to FIG. 2 , one or more database tables in database 120may include, as a non-limiting example, a conditions table 204, whichmay be used to store records and attributes related to medicalconditions. This may include, but not limited to, symptoms of theconditions, demographic of conditions, treatments, or the like. Asanother non-limiting example, one or more tables in database 120 mayinclude a compositions table 208 which may be used to store regenerativetreatment compositions 124 used to treat medical conditions, frequencyof administration of treatment, and the like. As another non-limitingexample, one or more tables in database 120 may include a biomarkerstable 212. A biomarkers table 212 may include, but not limited tocorrelations of biomarkers to conditions, values of biomarkersreflecting the presence of a condition, data on biomarker research, andthe like. As another non-limiting example, one or more tables indatabase 120 may include a user historical data table 216. A userhistorical data table 216 may include data from prior regenerativetreatments administered to users, user outcome based on the treatment,frequency of treatment received for a particular user, and the like.

Referring now to FIG. 3 , an exemplary embodiment of a machine-learningmodule 300 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 304 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 308 given data provided as inputs 312;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 3 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 304 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 304 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 304 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 304 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 304 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 304 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data304 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 3 ,training data 304 may include one or more elements that are notcategorized; that is, training data 304 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 304 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 304 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 304 used by machine-learning module 300 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample, biomarkers indicative of glycocalyx degradation may serve asinputs, outputting other potential health disorders that a may use thesame disease biomarkers.

Further referring to FIG. 3 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 316. Training data classifier 316 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 300 may generate aclassifier using a classification algorithm, defined as a processwhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 304. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 316 may classify elements of training data to classifybiomarkers indicative of glycocalyx degradation into categories such as,for example, a target organ, and the like.

Still referring to FIG. 3 , machine-learning module 300 may beconfigured to perform a lazy-learning process 320 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 304. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 304 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 3 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning model 324. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above and stored in memory; an inputis submitted to a machine-learning model 324 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 324 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 304set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 3 , machine-learning algorithms may include atleast a supervised machine-learning process 328. At least a supervisedmachine-learning process 328, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude a disease biomarker such as TNF-α and a concentration outside asuitable range, renal disorder as an outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 304. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process328 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 3 , machine learning processes may include atleast an unsupervised machine-learning processes 332. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 3 , machine-learning module 300 may be designedand configured to create a machine-learning model 324 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g., a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g., a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 3 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

With continued reference to FIG. 3 , a “classifier,” as used in thisdisclosure, is a machine-learning model, such as a mathematical model,neural net, or program generated by a machine learning algorithm knownas a “classification algorithm,” as described in further detail below,that sorts inputs into categories or bins of data, outputting thecategories or bins of data and/or labels associated therewith. Aclassifier may be configured to output at least a datum that labels orotherwise identifies a set of data that are clustered together, found tobe close under a distance metric as described below, or the like.Computing device 104 and/or another device may generate a classifierusing a classification algorithm, defined as a process whereby acomputing device 104 derives a classifier from training data.Classification may be performed using, without limitation, linearclassifiers such as without limitation logistic regression and/or naiveBayes classifiers, nearest neighbor classifiers such as k-nearestneighbors classifiers, support vector machines, least squares supportvector machines, fisher's linear discriminant, quadratic classifiers,decision trees, boosted trees, random forest classifiers, learningvector quantization, and/or neural network-based classifiers.

Still referring to FIG. 3 , computing device 104 may be configured togenerate a classifier using a Naïve Bayes classification algorithm.Naïve Bayes classification algorithm generates classifiers by assigningclass labels to problem instances, represented as vectors of elementvalues. Class labels are drawn from a finite set. Naïve Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naïve Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A) P(A)÷P(B), where P(AB) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naïve Bayes algorithm may be generatedby first transforming training data into a frequency table. Computingdevice 104 may then calculate a likelihood table by calculatingprobabilities of different data entries and classification labels.Computing device 104 may utilize a naïve Bayes equation to calculate aposterior probability for each class. A class containing the highestposterior probability is the outcome of prediction. Naïve Bayesclassification algorithm may include a gaussian model that follows anormal distribution. Naïve Bayes classification algorithm may include amultinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 3 , computing device 104 may beconfigured to generate a classifier using a K-nearest neighbors (KNN)algorithm. A “K-nearest neighbors algorithm” as used in this disclosure,includes a classification method that utilizes feature similarity toanalyze how closely out-of-sample-features resemble training data toclassify input data to one or more clusters and/or categories offeatures as represented in training data; this may be performed byrepresenting both training data and input data in vector forms, andusing one or more measures of vector similarity to identifyclassifications within training data, and to determine a classificationof input data. K-nearest neighbors algorithm may include specifying aK-value, or a number directing the classifier to select the k mostsimilar entries training data to a given sample, determining the mostcommon classifier of the entries in the database, and classifying theknown sample; this may be performed recursively and/or iteratively togenerate a classifier that may be used to classify input data as furthersamples. For instance, an initial set of samples may be performed tocover an initial heuristic and/or “first guess” at an output and/orrelationship, which may be seeded, without limitation, using expertinput received according to any process as described herein. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data. Heuristic mayinclude selecting some number of highest-ranking associations and/ortraining data elements.

With continued reference to FIG. 3 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non- limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)a_(i) ²)}, wherea_(i) is attribute number i of the vector. Scaling and/or normalizationmay function to make vector comparison independent of absolutequantities of attributes, while preserving any dependency on similarityof attributes; this may, for instance, be advantageous where casesrepresented in training data are represented by different quantities ofsamples, which may result in proportionally equivalent vectors withdivergent values.

Now referring to FIG. 4 , an exemplary replacement therapy treatment 400is disclosed. Replacement therapy treatment 400 may include solution ofelectrolytes 404. In this disclosure, “solution of electrolytes”consists of a liquid or solid phase containing at least one solvent,such as water, and an ionizable substance component, such as salt oracid. Solution of electrolytes 404 may include a mixture including butnot limited to one or more micronutrients and/or mineral ions such assodium chloride, sodium lactate, potassium chloride, calcium chloride,magnesium chloride, calcium gluconate, magnesium chloride, magnesiumsulphate, magnesium gluconate, magnesium threonate, zinc sulphate,copper gluconate, copper chloride, copper sulphate and the like.Replacement therapy treatment 400 may include protein 408. As usedherein, a “protein” is a large biomolecules and macromolecules thatcomprise one or more long chains of amino acid residues. The protein mayinclude an albumin. “Albumin” is a simple form of protein that issoluble in water and coagulable by heat. Albumin may be present in anamount between about 30 g/L to about 60 g/L, between about 35 g/L toabout 55 g/L, or between about 40 g/L to about 50 g/L. The compositionof the replacement therapy treatment may include an amount of albumin atleast about 60 g/L or at least about 55 g/L. Protein 408 may beglobulins. Examples of globulins include, but not limited toimmunoglobulin G (IgG), immunoglobulin M (IgM), immunoglobulin A (IgA),immunoglobulin D (IgD), immunoglobulin E (IgE), and the like. In anon-limiting example, the composition of the replacement therapytreatment may include intravenous immunoglobulin. Non-limiting examplesof intravenous immunoglobulin may include, but not limited toimmunoglobulin G1 (IgG1), immunoglobulin G2 (IgG2), immunoglobulin G3(IgG3), immunoglobulin G4 (IgG4), or combinations thereof. A totalamount of globulins may include an amount between about 30 g to about 50g, between about 35 g to about 40 g, or between about 30 g to about 40g. In an embodiment, one or more globulins may be infused to a patientin a particular order and/or at a specified dose. For example, a patientmay receive 20 g of albumin infused. In yet another non-limitingexample, a patient may receive an infusion of 20 g of albumin alternatedwith an infusion of 10 g IVIG 5%. In yet another non-limiting example,the composition of the replacement therapy treatment may include annumber of globulins at least about 1 g/L to about 20 g/L. Protein 408may include one or more ingredients including but not limited tofibrinogen, fresh frozen plasma, and/or fresh plasma derived from one ormore donors having specified characteristics such as a particular age,sex, disease history, infectious disease history, and the like.Fibrinogen may be present in an amount between about 150 mg/dl to about400 mg/dl, between about 250 mg/dl to about 300 mg/dl, or between about350 mg/dl to about 400 mg/dl.

Additionally, and with continued reference to FIG. 4 , the compositionof replacement therapy treatment 400 may include lipid 412. As used inthis disclosure, a “lipid” is a macro biomolecule that is soluble innonpolar solvents. This may include, but not limited to, phospholipidfatty acid such as lysophosphatidylcholine, phosphatidylcholine,phosphatidylethanolamine, phosphatidylinositol, sphingomyelin and thelike; fat-soluble vitamins (like vitamins A, D, E, K); a steroid, andthe like. A phospholipid may be present in an amount between about 1.0mg/m L to about 2.0 mg/ml, or between about 1.0 mg/ml to about 2.2, orbetween about 1.5 mg/ml to about 2.2.

Additionally or alternatively, and still with reference to FIG. 4 , thecomposition for the replacement therapy treatment 400 may includeanticoagulant 416. As used herein, an “anticoagulant” is a chemicalsubstance that prevent or reduce coagulation of blood, or in otherwords, a blood thinner. Examples of anticoagulants 416 may include, butnot limited to ethylenediaminetetraacetic acid (EDTA), sodium citrate,citrate dextrose, Heparin, Enoxaparin, Dalteparin, Nadroparin, and thelike.

Additionally, or alternatively, and with continued reference to FIG. 4 ,the composition for replacement therapy treatment 400 may includeadditives 420. “Additives” include chemical substances added to foods toproduce specific desirable effects. For example, additives 420 mayinclude a surfactant. Additives 420 may include a stabilizer. Examplesof stabilizer include, but are not limited to, PVP (Povidone), PVA(Polyvinyl alcohol), PEG (Polyethylene glycol), HPMC (Hypromellose), HPC(Hydroxypropyl cellulose), HEC (Hydroxyethyl cellulose), NaCMC(Carboxymethylcellulose sodium), SD (Docusate sodium), SLS (Sodiumlauryl sulfate), PEI (Polyethylene imine), TPGS (D-α-tocopherylpolyethylene glycol succinate), PEO (Polyethylene oxide) or PPO(Polypropylene oxide), and combinations thereof.

With continued reference to FIG. 4 , the composition for replacementtherapy treatment 400 may include one or more additional ingredients. An“additional ingredient” as used in this disclosure may include asupplemental ingredient added to replacement therapy treatment 400. Anadditional ingredient may include but is not limited to a protein, aminoacid, organic acid, bioidentical compound, biosimilar compound, ahormone, a cell-signaling molecule, RNA, DNA, anti-sense RNA, and/or anyother pharmaceutical and/or non-pharmaceutical ingredient.

With continued reference to FIG. 4 , the composition for replacementtherapy treatment 400 may include one or more vitamins, minerals, and/oradditional ingredients. This may include but is not limited to anyvitamin, mineral, alpha lipoic acid, NADH, glutathione, ions,resveratrol, Coenzyme Q10, ubiquinol, 1-arginine and/or any ingredientderived from a biological source.

With continued reference to FIG. 4 , the composition for the replacementtherapy treatment replacement therapy may include stem cells. A “stemcell” as used in this specification, is a cell that has the ability todevelop into a specialized cell and replace cells or tissue that hasbeen damaged. Stem cells may be adult stem cells. “Adult stem cells” asused in this disclosure are stem cells obtained for certain regions ofthe adult body such as, but not limited to, the epidermis of the skin,the lining of the small intestine, the bone marrow, and the like. Stemcells may be pluripotent. A “pluripotent stem cell” as used in thisdisclosure, is a stem cell that has the ability to undergo self-renewaland to give rise to all cells of the tissues in the body. A stem cellmay include an exosome. An “exosome,” as used in this disclosure, is anextracellular vesicle produced in an endosomal compartment of aeukaryotic cell. A stem cell may include a very small embryonic likestem cell (VSEL). In an embodiment, a stem cell may be produced and/orgenerated using 3-D printing technology.

Now referring to FIG. 5 , a flow diagram illustrating an exemplaryembodiment of a method 500 for determining a composition of areplacement therapy treatment is illustrated. Method 500 may beperformed by a computing device 104. Composition 124 may be any of thecompositions described herein with reference to FIGS. 1 and 2 .Computing device 104 may be any of the computing devices describedherein with reference to FIGS. 1 and 6 .

Still referring to FIG. 5 , at step 505, method 500 includes receiving,at a computing device 104, a user input 108 wherein user input 108comprises at least an identifier and a constitutional history of theuser. User input 108 may be received by a user device 112. Computingdevice 104 may be any of the computing devices described herein withreference to FIGS. 1 and 6 . User input 108 may be any of the inputsdescribed herein with reference to FIGS. 1 and 2 . User device 112 maybe any of the devices described herein with reference to FIG. 1 .

Still referring to FIG. 5 , at step 510, method 500 includes generating,at computing device 104, a first condition descriptor 116 as a functionof user input 108. Generating the first condition descriptor comprisesidentifying a plurality of biomarkers as a function of the user input,calculating a value for each biomarker of the plurality of biomarkers ina biological sample, training a second machine-learning process using areference biomarker training data wherein the reference biomarkertraining data correlates reference values for each biomarker to thefirst conditions, and determining the first condition descriptor as afunction of the value for each biomarker of the plurality of biomarkersand the second machine learning process. Generating the first conditiondescriptor further comprises regenerate, iteratively, the referencebiomarker training data as a function of the first condition descriptorand retrain the second machine-learning process using regeneratedbiomarker training data. Plurality of biomarkers comprise at least adiagnostic biomarker. Computing device 104 may be any of the computingdevices described herein with reference to FIGS. 1 and 6 . Firstcondition descriptor 116 may be an of the conditions described hereinwith reference to FIG. 1 . User input 108 may be any of the inputsdescribed herein with reference to FIGS. 1 and 2 .

Still referring to FIG. 5 , at step 515, method 500 includesdetermining, at a computing device 104, a composition 124 of areplacement therapy treatment as a function of the first conditiondescriptor 116. Composition 124 of the replacement therapy treatmentcomprises albumin. Albumin is present in an amount ranging from at leastabout 30 grams per liter to about 60 grams per liter. Computing device104 may be any of the computing devices described herein with referenceto FIGS. 1 and 6 . Composition 124 may be any of the compositionsdescribed herein with reference to FIGS. 1 and 2 . First conditiondescriptor 116 may be an of the conditions described herein withreference to FIG. 1 .

Still referring to FIG. 5 , at step 520, method 500 includes outputting,at a computing device 104, composition 124 of the replacement therapytreatment as a function of the determination. composition of thereplacement therapy treatment comprises a solution of electrolytes.composition of the replacement therapy treatment comprises lipids.Computing device 104 may be any of the computing devices describedherein with reference to FIGS. 1 and 6 . Composition 124 may be any ofthe compositions described herein with reference to FIGS. 1 and 2 .

Still referring to FIG. 5 , method 500 may further comprise a robotdesigned and configured to prepare the composition of the replacementtherapy treatment for a user. Robot may be any of the robots designedherein with reference to FIG. 1 . Composition 124 may be any of thecompositions described herein with reference to FIGS. 1 and 2 .

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random-access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 6 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 600 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 600 includes a processor 604 and a memory608 that communicate with each other, and with other components, via abus 612. Bus 612 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 604 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 604 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 604 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating-pointunit (FPU), and/or system on a chip (SoC).

Memory 608 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 616 (BIOS), including basic routines that help totransfer information between elements within computer system 600, suchas during start-up, may be stored in memory 608. Memory 608 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 620 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 608 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 600 may also include a storage device 624. Examples of astorage device (e.g., storage device 624) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 624 may be connected to bus 612 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 624 (or one or morecomponents thereof) may be removably interfaced with computer system 600(e.g., via an external port connector (not shown)). Particularly,storage device 624 and an associated machine-readable medium 628 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 600. In one example, software 620 may reside, completelyor partially, within machine-readable medium 628. In another example,software 620 may reside, completely or partially, within processor 604.

Computer system 600 may also include an input device 632. In oneexample, a user of computer system 600 may enter commands and/or otherinformation into computer system 600 via input device 632. Examples ofan input device 632 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 632may be interfaced to bus 612 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 612, and any combinations thereof. Input device 632 mayinclude a touch screen interface that may be a part of or separate fromdisplay 636, discussed further below. Input device 632 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 600 via storage device 624 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 640. A network interfacedevice, such as network interface device 640, may be utilized forconnecting computer system 600 to one or more of a variety of networks,such as network 644, and one or more remote devices 648 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 644,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 620,etc.) may be communicated to and/or from computer system 600 via networkinterface device 640.

Computer system 600 may further include a video display adapter 652 forcommunicating a displayable image to a display device, such as displaydevice 636. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 652 and display device 636 may be utilized incombination with processor 604 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 600 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 612 via a peripheral interface 656. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods andsystems, according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. An apparatus for determining a composition of areplacement therapy treatment, the apparatus comprising: at least aprocessor; and a memory communicatively connected to the processor, thememory containing instructions configuring the at least a processor to:receive a user input, wherein the user input comprises at least anidentifier and a constitutional history of the user; generate a firstcondition descriptor as a function of the user input; determine acomposition of a replacement therapy treatment as a function of thefirst condition descriptor, wherein determining comprises: training afirst machine-learning process using user training data, wherein theuser training data correlates user input data to exemplary compositionsof replacement therapy treatment; and determining the composition as afunction of the user input and the first machine learning process; andoutput the composition of the replacement therapy treatment as afunction of the determination.
 2. The apparatus of claim 1, furthercomprising a database to retrieve the user input from.
 3. The apparatusof claim 1, wherein generating the first condition descriptor furthercomprises: identifying a plurality of biomarkers as a function of theuser input; calculating a value for each biomarker of the plurality ofbiomarkers in a biological sample; training a second machine-learningprocess using a reference biomarker training data, wherein the referencebiomarker training data correlates reference values for each biomarkerto condition data; and determining the first condition descriptor as afunction of the value for each biomarker and the second machine learningprocess.
 4. The apparatus of claim 3, wherein generating the firstcondition descriptor further comprises: regenerate, iteratively, thereference biomarker training data as a function of the first condition;and retrain the second machine-learning process using regeneratedbiomarker training data.
 5. The apparatus of claim 3, wherein theplurality of biomarkers comprise at least a diagnostic biomarker.
 6. Theapparatus of claim 1, wherein the composition of the replacement therapytreatment comprises albumin in an amount ranging from at least 30 gramsper liter to about 60 grams per liter.
 7. The apparatus of claim 1,wherein the composition of the replacement therapy treatment comprisesat least an immunoglobulin present in an amount ranging from about 0.01g/liter to about 20 g/liter.
 8. The apparatus of claim 1, wherein thecomposition of the replacement therapy treatment comprises a solution ofelectrolytes.
 9. The apparatus of claim 1, wherein the composition ofthe replacement therapy treatment comprises lipids.
 10. The apparatus ofclaim 1, further comprising a robot designed and configured to preparethe composition of the replacement therapy treatment for a user.
 11. Amethod for determining a composition of a replacement therapy treatment,the method comprises: receiving, at a processor, a user input, whereinthe user input comprises at least an identifier and a constitutionalhistory of the user; generating, at the processor, a first conditiondescriptor as a function of the user input; determining, at a processor,a composition of a replacement therapy treatment as a function of thefirst condition, wherein the determination comprises: training a firstmachine-learning process using user training data, wherein the usertraining data correlates user inputs to compositions of the replacementtherapy treatment; and determining the composition as a function of theuser input and the first machine learning process; and outputting, at aprocessor, the composition of the replacement therapy treatment as afunction of the determination.
 12. The method of claim 11, furthercomprising a database to retrieve the user input from.
 13. The method ofclaim 11, wherein generating the first condition descriptor furthercomprises: identifying a plurality of biomarkers as a function of theuser input; calculating a value for each biomarker of the plurality ofbiomarkers in a biological sample; training a second machine-learningprocess using a reference biomarker training data, wherein the referencebiomarker training data correlates reference values for each biomarkerto the first conditions; and determining the first condition descriptoras a function of the value for each biomarker of the plurality ofbiomarkers and the second machine learning process.
 14. The method ofclaim 13, wherein generating the first condition descriptor furthercomprises: regenerating, iteratively, the reference biomarker trainingdata as a function of the first condition; and retraining the secondmachine-learning process using regenerated biomarker training data. 15.The method of claim 13, wherein the plurality of biomarkers comprise atleast a diagnostic biomarker.
 16. The method of claim 11, wherein thecomposition of the replacement therapy treatment comprises albuminpresent in an amount ranging from about 30 grams to about 60 grams. 17.The method of claim 11, wherein the composition of the replacementtherapy treatment comprises at least an immunoglobulin present in anamount ranging from about 0.01 g/liter to about 20 g/liter. albumin ispresent in an amount ranging from at least about 30 grams per liter toabout 60 grams per liter.
 18. The method of claim 11, wherein thecomposition of the replacement therapy treatment comprises a solution ofelectrolytes.
 19. The method of claim 11, wherein the composition of thereplacement therapy treatment comprises lipids.
 20. The method of claim11, further comprising a robot designed and configured to prepare thecomposition of the replacement therapy treatment for a user.